// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
// Copyright (C) 2018 Mehdi Goli <eigen@codeplay.com> Codeplay Software Ltd.
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
#define EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H

namespace Eigen {
namespace internal {

namespace {

EIGEN_DEVICE_FUNC uint64_t
get_random_seed()
{
#if defined(EIGEN_GPU_COMPILE_PHASE)
	// We don't support 3d kernels since we currently only use 1 and
	// 2d kernels.
	gpu_assert(threadIdx.z == 0);
	return blockIdx.x * blockDim.x + threadIdx.x + gridDim.x * blockDim.x * (blockIdx.y * blockDim.y + threadIdx.y);
#else
	// Rely on Eigen's random implementation.
	return random<uint64_t>();
#endif
}

static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE unsigned
PCG_XSH_RS_generator(uint64_t* state, uint64_t stream)
{
	// TODO: Unify with the implementation in the non blocking thread pool.
	uint64_t current = *state;
	// Update the internal state
	*state = current * 6364136223846793005ULL + (stream << 1 | 1);
	// Generate the random output (using the PCG-XSH-RS scheme)
	return static_cast<unsigned>((current ^ (current >> 22)) >> (22 + (current >> 61)));
}

static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE uint64_t
PCG_XSH_RS_state(uint64_t seed)
{
	seed = seed ? seed : get_random_seed();
	return seed * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL;
}

} // namespace

template<typename T>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T
RandomToTypeUniform(uint64_t* state, uint64_t stream)
{
	unsigned rnd = PCG_XSH_RS_generator(state, stream);
	return static_cast<T>(rnd);
}

template<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half
RandomToTypeUniform<Eigen::half>(uint64_t* state, uint64_t stream)
{
	// Generate 10 random bits for the mantissa, merge with exponent.
	unsigned rnd = PCG_XSH_RS_generator(state, stream);
	const uint16_t half_bits = static_cast<uint16_t>(rnd & 0x3ffu) | (static_cast<uint16_t>(15) << 10);
	Eigen::half result = Eigen::numext::bit_cast<Eigen::half>(half_bits);
	// Return the final result
	return result - Eigen::half(1.0f);
}

template<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::bfloat16
RandomToTypeUniform<Eigen::bfloat16>(uint64_t* state, uint64_t stream)
{

	// Generate 7 random bits for the mantissa, merge with exponent.
	unsigned rnd = PCG_XSH_RS_generator(state, stream);
	const uint16_t half_bits = static_cast<uint16_t>(rnd & 0x7fu) | (static_cast<uint16_t>(127) << 7);
	Eigen::bfloat16 result = Eigen::numext::bit_cast<Eigen::bfloat16>(half_bits);
	// Return the final result
	return result - Eigen::bfloat16(1.0f);
}

template<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float
RandomToTypeUniform<float>(uint64_t* state, uint64_t stream)
{
	typedef union
	{
		uint32_t raw;
		float fp;
	} internal;
	internal result;
	// Generate 23 random bits for the mantissa mantissa
	const unsigned rnd = PCG_XSH_RS_generator(state, stream);
	result.raw = rnd & 0x7fffffu;
	// Set the exponent
	result.raw |= (static_cast<uint32_t>(127) << 23);
	// Return the final result
	return result.fp - 1.0f;
}

template<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double
RandomToTypeUniform<double>(uint64_t* state, uint64_t stream)
{
	typedef union
	{
		uint64_t raw;
		double dp;
	} internal;
	internal result;
	result.raw = 0;
	// Generate 52 random bits for the mantissa
	// First generate the upper 20 bits
	unsigned rnd1 = PCG_XSH_RS_generator(state, stream) & 0xfffffu;
	// The generate the lower 32 bits
	unsigned rnd2 = PCG_XSH_RS_generator(state, stream);
	result.raw = (static_cast<uint64_t>(rnd1) << 32) | rnd2;
	// Set the exponent
	result.raw |= (static_cast<uint64_t>(1023) << 52);
	// Return the final result
	return result.dp - 1.0;
}

template<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<float>
RandomToTypeUniform<std::complex<float>>(uint64_t* state, uint64_t stream)
{
	return std::complex<float>(RandomToTypeUniform<float>(state, stream), RandomToTypeUniform<float>(state, stream));
}
template<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<double>
RandomToTypeUniform<std::complex<double>>(uint64_t* state, uint64_t stream)
{
	return std::complex<double>(RandomToTypeUniform<double>(state, stream), RandomToTypeUniform<double>(state, stream));
}

template<typename T>
class UniformRandomGenerator
{
  public:
	static const bool PacketAccess = true;

	// Uses the given "seed" if non-zero, otherwise uses a random seed.
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator(uint64_t seed = 0)
	{
		m_state = PCG_XSH_RS_state(seed);
#ifdef EIGEN_USE_SYCL
		// In SYCL it is not possible to build PCG_XSH_RS_state in one step.
		// Therefor, we need two step to initializate the m_state.
		// IN SYCL, the constructor of the functor is s called on the CPU
		// and we get the clock seed here from the CPU. However, This seed is
		// the same for all the thread. As unlike CUDA, the thread.ID, BlockID, etc is not a global function.
		// and only  available on the Operator() function (which is called on the GPU).
		// Thus for CUDA (((CLOCK  + global_thread_id)* 6364136223846793005ULL) + 0xda3e39cb94b95bdbULL) is passed to
		// each thread but for SYCL ((CLOCK * 6364136223846793005ULL) + 0xda3e39cb94b95bdbULL) is passed to each thread
		// and each thread adds the  (global_thread_id* 6364136223846793005ULL) for itself only once, in order to
		// complete the construction similar to CUDA Therefore, the thread Id injection is not available at this stage.
		// However when the operator() is called the thread ID will be avilable. So inside the opeator,
		// we add the thrreadID, BlockId,... (which is equivalent of i)
		// to the seed and construct the unique m_state per thead similar to cuda.
		m_exec_once = false;
#endif
	}
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UniformRandomGenerator(const UniformRandomGenerator& other)
	{
		m_state = other.m_state;
#ifdef EIGEN_USE_SYCL
		m_exec_once = other.m_exec_once;
#endif
	}

	template<typename Index>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(Index i) const
	{
#ifdef EIGEN_USE_SYCL
		if (!m_exec_once) {
			// This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
			// The (i * 6364136223846793005ULL) is the remaining part of the PCG_XSH_RS_state on the GPU side
			m_state += (i * 6364136223846793005ULL);
			m_exec_once = true;
		}
#endif
		T result = RandomToTypeUniform<T>(&m_state, i);
		return result;
	}

	template<typename Packet, typename Index>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(Index i) const
	{
		const int packetSize = internal::unpacket_traits<Packet>::size;
		EIGEN_ALIGN_MAX T values[packetSize];
#ifdef EIGEN_USE_SYCL
		if (!m_exec_once) {
			// This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
			m_state += (i * 6364136223846793005ULL);
			m_exec_once = true;
		}
#endif
		EIGEN_UNROLL_LOOP
		for (int j = 0; j < packetSize; ++j) {
			values[j] = RandomToTypeUniform<T>(&m_state, i);
		}
		return internal::pload<Packet>(values);
	}

  private:
	mutable uint64_t m_state;
#ifdef EIGEN_USE_SYCL
	mutable bool m_exec_once;
#endif
};

template<typename Scalar>
struct functor_traits<UniformRandomGenerator<Scalar>>
{
	enum
	{
		// Rough estimate for floating point, multiplied by ceil(sizeof(T) / sizeof(float)).
		Cost = 12 * NumTraits<Scalar>::AddCost * ((sizeof(Scalar) + sizeof(float) - 1) / sizeof(float)),
		PacketAccess = UniformRandomGenerator<Scalar>::PacketAccess
	};
};

template<typename T>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T
RandomToTypeNormal(uint64_t* state, uint64_t stream)
{
	// Use the ratio of uniform method to generate numbers following a normal
	// distribution. See for example Numerical Recipes chapter 7.3.9 for the
	// details.
	T u, v, q;
	do {
		u = RandomToTypeUniform<T>(state, stream);
		v = T(1.7156) * (RandomToTypeUniform<T>(state, stream) - T(0.5));
		const T x = u - T(0.449871);
		const T y = numext::abs(v) + T(0.386595);
		q = x * x + y * (T(0.196) * y - T(0.25472) * x);
	} while (q > T(0.27597) && (q > T(0.27846) || v * v > T(-4) * numext::log(u) * u * u));

	return v / u;
}

template<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<float>
RandomToTypeNormal<std::complex<float>>(uint64_t* state, uint64_t stream)
{
	return std::complex<float>(RandomToTypeNormal<float>(state, stream), RandomToTypeNormal<float>(state, stream));
}
template<>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::complex<double>
RandomToTypeNormal<std::complex<double>>(uint64_t* state, uint64_t stream)
{
	return std::complex<double>(RandomToTypeNormal<double>(state, stream), RandomToTypeNormal<double>(state, stream));
}

template<typename T>
class NormalRandomGenerator
{
  public:
	static const bool PacketAccess = true;

	// Uses the given "seed" if non-zero, otherwise uses a random seed.
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(uint64_t seed = 0)
	{
		m_state = PCG_XSH_RS_state(seed);
#ifdef EIGEN_USE_SYCL
		// In SYCL it is not possible to build PCG_XSH_RS_state in one step.
		// Therefor, we need two steps to initializate the m_state.
		// IN SYCL, the constructor of the functor is s called on the CPU
		// and we get the clock seed here from the CPU. However, This seed is
		// the same for all the thread. As unlike CUDA, the thread.ID, BlockID, etc is not a global function.
		// and only  available on the Operator() function (which is called on the GPU).
		// Therefore, the thread Id injection is not available at this stage. However when the operator()
		// is called the thread ID will be avilable. So inside the opeator,
		// we add the thrreadID, BlockId,... (which is equivalent of i)
		// to the seed and construct the unique m_state per thead similar to cuda.
		m_exec_once = false;
#endif
	}
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE NormalRandomGenerator(const NormalRandomGenerator& other)
	{
		m_state = other.m_state;
#ifdef EIGEN_USE_SYCL
		m_exec_once = other.m_exec_once;
#endif
	}

	template<typename Index>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(Index i) const
	{
#ifdef EIGEN_USE_SYCL
		if (!m_exec_once) {
			// This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
			m_state += (i * 6364136223846793005ULL);
			m_exec_once = true;
		}
#endif
		T result = RandomToTypeNormal<T>(&m_state, i);
		return result;
	}

	template<typename Packet, typename Index>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(Index i) const
	{
		const int packetSize = internal::unpacket_traits<Packet>::size;
		EIGEN_ALIGN_MAX T values[packetSize];
#ifdef EIGEN_USE_SYCL
		if (!m_exec_once) {
			// This is the second stage of adding thread Id to the CPU clock seed and build unique seed per thread
			m_state += (i * 6364136223846793005ULL);
			m_exec_once = true;
		}
#endif
		EIGEN_UNROLL_LOOP
		for (int j = 0; j < packetSize; ++j) {
			values[j] = RandomToTypeNormal<T>(&m_state, i);
		}
		return internal::pload<Packet>(values);
	}

  private:
	mutable uint64_t m_state;
#ifdef EIGEN_USE_SYCL
	mutable bool m_exec_once;
#endif
};

template<typename Scalar>
struct functor_traits<NormalRandomGenerator<Scalar>>
{
	enum
	{
		// On average, we need to generate about 3 random numbers
		// 15 mul, 8 add, 1.5 logs
		Cost = 3 * functor_traits<UniformRandomGenerator<Scalar>>::Cost + 15 * NumTraits<Scalar>::AddCost +
			   8 * NumTraits<Scalar>::AddCost + 3 * functor_traits<scalar_log_op<Scalar>>::Cost / 2,
		PacketAccess = NormalRandomGenerator<Scalar>::PacketAccess
	};
};

} // end namespace internal
} // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_RANDOM_H
